Exploring the impact of high pressure processing on the characteristics of processed fruit and vegetable products: a comprehensive review
Bibliographic record
Abstract
Consumers are increasingly interested in additive-free products with a fresh taste, leading to a growing trend in high pressure processing (HPP) as an alternative to thermal processing. This review explores the impact of HPP on the properties of juices, smoothies, and purees, as well as its practical applications in the food industry. Research findings have explained that HPP is a most promising technology in comparison to thermal processing, in two ways i.e., for ensuring microbial safety and maximum retention of micro and macro nutrients and functional components. HPP preserves natural color and eliminates the need for artificial coloring. The review also emphasizes its potential for enhancing flavor in the beverage industry. The review also discusses how HPP indirectly affects plant enzymes that cause off-flavors and suggests potential hurdle approaches for enzyme inactivation based on research investigations. Scientific studies regarding the improved quality insights on commercially operated high pressure mechanisms concerning nutrient retention have paved the way for upscaling and boosted the market demand for HPP equipment. In future research, the clear focus should be on scientific parameters and sensory attributes related to consumer acceptability and perception for better clarity of the HPP effect on juice and smoothies/purees.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".